Power Plant Dispatch Control System for Fleet Optimization
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Solution Overview
Problem
Current control systems in the power generation industry fail to fully leverage available operational data for optimizing power plant efficiency and economic return, leading to inefficient operations and suboptimal performance due to complexity and variability in generating units and market conditions.
Innovation Solution
A system and method that utilize a distributed computing network and user devices to create and manage generating plans, allowing for real-time data analysis and optimization of power plant operations, including graphical user interfaces for modifying plans and submitting bids, which are based on actual performance capabilities and fleet capacity, enabling more accurate and competitive dispatch bids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional control systems are used to manage power plant operations, then system simplicity is maintained, but operational efficiency and economic return are insufficient due to inability to leverage available operational data
Solution Approach 1:
The control system is segmented into multiple hierarchical levels: unit-level control systems for individual generating units, plant-level control systems for overall plant coordination, and fleet-level control systems for multi-plant optimization. This segmentation allows complex data analytics to be distributed across manageable modules, improving operational efficiency without overwhelming system complexity.
Solution Approach 2:
An intermediary control layer is introduced between the traditional control systems and the operational data sources. This intermediary layer collects, processes, and analyzes operational data from multiple sources, then translates insights into actionable control commands, bridging the gap between data availability and operational decision-making.
2Measurement precision
If real-time data analysis is implemented to improve operational intelligence, then measurement precision of plant capabilities is enhanced, but device complexity increases due to additional computing and data processing requirements
Solution Approach 1:
Operational data is collected, processed, and analyzed in advance of critical decision-making moments. The system pre-calculates performance metrics, identifies trends, and prepares optimization recommendations before they are needed for dispatch decisions, reducing the complexity of real-time processing while maintaining high measurement precision.
Solution Approach 2:
Virtual models and digital twins of power plant systems are created to replicate and analyze operational data. These computational copies allow complex data processing and simulation without directly impacting the physical system, enabling high-precision measurement and analysis while isolating the complexity in the virtual domain.
3Adaptability or versatility
If multiple generating configurations are utilized to maximize fleet capacity, then adaptability of the power system is improved, but difficulty of control and coordination increases
Solution Approach 1:
The control system dynamically adapts generating configurations based on real-time operational conditions, market demands, and plant capabilities. Rather than managing all possible configurations statically, the system continuously adjusts the optimal configuration mix, reducing control complexity by focusing only on dynamically relevant options while maintaining high adaptability.
Solution Approach 2:
A closed-loop feedback mechanism is implemented where operational results from various generating configurations are continuously monitored and fed back to the control system. This feedback enables automatic adjustment and optimization of configuration selections, reducing the complexity of manual coordination while maximizing adaptability to changing conditions.
Data Source
AI summary
A method for controlling a power plant that includes: presenting on a first user device a proposed version of the generating plan for a future generating period; receiving at the first user device a user input making a first modification to the proposed version to create a bid version of the generating plan; presenting on a second user device the bid version of the generating plan so that an indicator indicates the aspect that was modified by the first modification; receiving a user input on the second user device for creating a bid based on the bid version of the generating plan; transmitting from the second user device the bid; receiving at the second user device a response comprising an awarded fleet capacity; and transmitting from the second user device to the first user device an awarded plant-level capacity based on the awarded fleet capacity.